Planning the site layout of construction projects is a crucial task that has a significant impact on construction cost, productivity, and safety. It involves the positioning and dynamic relocation of temporary facilities that are needed to support various construction activities on site such as offices, storage areas, workshops, and parking areas. Due to the complexity of the site layout planning problem, construction managers often perform this task using previous experience, ad-hoc rules, and first-come-first-serve approach which leads to ambiguity and even to inefficiency. Accordingly, a number of site layout planning models have been developed over the past three decades to support this important planning task.
Despite the contributions of existing site layout planning models, they have a number of limitations that require additional research in five main areas in order to: (1) ensure global optimization of dynamic site layout planning; (2) integrate material procurement and site layout planning in a construction logistics planning model; (3) enhance the utilization of interior building spaces for material storage areas on congested construction sites; (4) enable automated retrieval and integration of all necessary data of construction logistics and site layout planning from available design and planning documents; and (5) consider security needs and constraints during the construction of critical infrastructure projects.
Accordingly, the main objectives of this study are to: (1) formulate novel models of dynamic site layout planning (DSLP) that are capable of generating global optimal solutions of DSLP problems by considering the effects of first stage layout decisions on the layouts of subsequent stages; (2) develop an innovative optimization model for construction logistics planning (CLP) that is capable of integrating and optimizing the critical planning decisions of material procurement and material storage on construction sites; (3) formulate a new multi-objective optimization model for Congested Construction Logistics Planning that is capable of modeling and utilizing interior and exterior spaces in order to generate optimal logistics plans for congested construction sites; (4) develop a multi-objective automated system for construction logistics optimization that enables seamless retrieval and integration of project spatial, temporal, and logistics data as well as generating and reporting optimal plans of material procurement and site layouts; and (5) formulate a multi-objective optimization framework for planning construction site layouts and site security systems of critical infrastructure projects.
First, two novel optimization models are developed that are capable of generating global optimal solutions of dynamic site layout planning in order to minimize resources travel and facilities relocation costs while complying with various site geometric constraints. The first model, DSLP-GA, is implemented using Genetic Algorithms while the second model, DSLP-ADP, is formulated using Approximate Dynamic Programming. These two models are designed to optimize facilities locations and orientations over construction stages to minimize total layout costs, which include the travel cost of construction resources and the cost of relocating temporary facilities between construction stages. Furthermore, the developed models consider four types of geometric constraints (boundary, overlap, distance, and zone constraints), which can be used to represent site space availability as well as construction operational and/or safety requirements. The performance of these two models is evaluated using two examples to illustrate their capabilities in generating global optimal plans solutions for dynamic site layout planning problems.
Second, a novel model of construction logistics planning (CLP) is developed to enable the integration and simultaneous optimization of critical planning decisions of material procurement and material storage on construction sites. Procurement decision variables are designed to identify the fixed-ordering-periods of each material in every construction stage, while dynamic layout decision variables are designed to identify the locations and orientations of material storage areas and other temporary facilities in each construction stage. The model utilizes Genetic Algorithms to generate optimal material procurement and layout decisions in order to minimize four types of construction logistics costs: material ordering, financing, stock-out, and layout costs. The performance of the developed CLP model is evaluated using an application example that illustrates the model capabilities in: (1) generating optimal procurement decisions that minimize ordering, financing, and stock-out costs while considering site space availability; and (2) generating optimal layout decisions that minimize layout costs while complying with material storage space needs as well as imposed operational and safety geometric constraints.
Third, an innovative multi-objective optimization model for congested construction logistics planning (C2LP) is developed to help planners in utilizing interior building spaces and generating optimal logistics plans that minimize total logistics cost while minimizing the adverse impacts of interior material storage on project schedule. Interior building space is represented as a set of non-identical rooms that can be defined based on project architectural drawings, while exterior space is modeled as a grid of locations with planner-specified fixed spacing. The model utilizes multi-objective Genetic Algorithms to formulate and optimize four categories of decision variables: (1) material procurement that includes fixed-ordering-periods of every material in each stage; (2) material storage plan that includes material storage type, exterior grid location, exterior orientation angle, and/or interior storage location for every material in each stage; (3) temporary facilities site layout that identifies exterior grid location and orientation angle for every temporary facility in each stage; and (4) schedule of noncritical activities that identifies the number of minimum-shifting-days within the total float of each non-critical activity. Interior material storage plans are generated using novel computational algorithms that consider four main types of interior storage constraints: room space capacities, room creation times, room partitioning times, and permissible material interior storage periods. Furthermore, new algorithms are developed to calculate interior and exterior material handling costs as well as shifting of noncritical activities. C2LP model utilizes Genetic Algorithms to generate optimal solutions that represent optimal tradeoffs between the two conflicting objectives of minimizing total logistics costs and project schedule criticality.
Fourth, a prototype automated multi-objective optimization system for construction logistics planning is implemented to support construction planners in generating optimal plans of material logistics and site layout. The system is developed in four main modules: (1) site spatial data retrieval module; (2) schedule data retrieval module; (3) relational database module; and (4) graphical user interface module. The site spatial data retrieval module is designed to facilitate the automated retrieval of site exterior dimensions and building geometric attributes (building footprint, floors, and rooms) from existing IFC-Based Building Information Models of the project. The schedule data retrieval module is designed to obtain the list of construction activities, their relationships, construction materials, and activities material demand from schedule database files that are exported from Microsoft Project. The relational database module is designed to store and integrate project spatial, temporal, and logistics input data considering their interdependencies in order to eliminate data inconsistencies. The user interface module is designed to facilitate data input and reporting of generated optimal material logistics plans.
Fifth, a multi-objective optimization framework is developed to enable construction planners of critical infrastructure projects to plan and optimize the implementation of site physical security systems and layout planning in order to minimize construction security risks and overall site costs. The framework is developed in four main phases: (1) risk identification and system modeling phase to identify security threats, attackers, and targets as well as site and security system geometric representation; (2) security lighting optimization phase to generate optimal tradeoff designs of fence and area lighting systems that consider the conflicting objectives of maximizing lighting performance while minimizing its system cost; (3) security-cost optimization phase to generate optimal site security systems that quantifies and simultaneously minimizes construction security risks and overall site cost; and (4) performance evaluation phase to test and analyze the performance of the proposed framework.
The aforementioned developments of this research study contribute to enhancing the current practices of site layout and material logistics planning and can lead to: (1) increasing the efficiency and global optimality of construction site layout planning; (2) improving construction productivity that can be realized as a result of the early coordination between material procurement and site space planning; (3) enhancing the utilization of interior building spaces for material storage areas while minimizing its possible negative impacts on construction operations and schedules; (4) increasing the security level on the construction sites of critical infrastructure projects; and (5) minimizing contractors site costs that cover the travel cost of resources on construction sites, material logistics, and site security systems.